Machine learning systems for modeling and balancing the activity of air quality devices in industrial applications

ABSTRACT

An indoor air quality control system may be implemented to control a plurality of air handling units within an industrial facility in a concerted effort to effect an overall air quality goal. A remote server analyzes sensor data, historical data, and other environmental data (e.g., predicted weather data), and uses one or more machine learning algorithms to model the behavior of air within the facility. The sensed air quality data is considered holistically to understand the overall condition of the facility and the gradient of air flows and/or contaminant flows within the 3-dimensional space. Air handling models are applied to current sensor data to generate instructions to selectively turn on/off or otherwise control components of various air handling equipment to reach an optimized air quality result. Decisions on how to control the facility are based on environmental health and safety considerations.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 17/185,154, entitled “Machine Learning Systems forModeling and Balancing the Activity of Air Quality Devices in IndustrialApplications” and filed on Feb. 25, 2021, which is incorporated hereinby reference.

BACKGROUND

Industrial facilities are often built with large open areas in whichboth workers and sensitive materials are present. In some cases, theprocesses performed in an industrial facility may negatively influencethe climate and air quality of the facility, making it hazardous orinhospitable to the environment inside or outside the facility, or maylead the air quality to exceed or contravene regulatory or environmentalhealth and safety requirements. To stop or mitigate negative effects onair quality, industrial facilities use air handling systems responsiblefor providing fresh, conditioned outside air (or recycled air) and/orremoving air from within the facility. Air handlers such as heating,ventilation and air conditioning (HVAC) units and make-up air units(MAUs) may be individually controlled via a thermostat or basic on/offswitches. The facilities may also be cleaned through some sort of stackor baghouse system or exhaust units.

Residential or commercial facilities may also use air handling systems,but unlike industrial facilities, residential or commercial facilitiesare generally focused on human comfort, i.e., keeping the facilitywithin a comfortable temperature for a person's living conditions.Residential facilities are often much smaller than industrialfacilities, and commercial facilities may have less large open indoorspace than industrial facilities. Further, both residential andcommercial facilities have less safety hazards than are present inindustrial environments. Accordingly, the air handling systems forresidential or commercial facilities (such as a single HVAC unit, or ajointly controlled set of identical HVAC units) are directed todifferent needs and considerations than those of industrialapplications.

Further techniques to effectively manage air quality in industrialapplications are generally desired.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure can be better understood with reference to the followingdrawings. The elements of the drawings are not necessarily to scalerelative to each other, emphasis instead being placed upon clearlyillustrating the principles of the disclosure. Furthermore, likereference numerals designate corresponding parts throughout the severalviews.

FIG. 1 is a diagram of an exemplary air handling system deployed in anindoor facility, in accordance with some embodiments of the presentdisclosure.

FIG. 2A is a block diagram of an exemplary wireless network inaccordance with some embodiments of the present disclosure.

FIG. 2B is a block diagram of an exemplary wireless mesh network inaccordance with some embodiments of the present disclosure.

FIG. 2C is a block diagram of an exemplary network of air handlingdevices, in accordance with some embodiments of the present disclosure.

FIG. 3A is a block diagram of certain component parts of a networkgateway device in accordance with some embodiments of the presentdisclosure.

FIG. 3B is a block diagram of certain component parts of a remote serverdevice in accordance with some embodiments of the present disclosure.

FIG. 4A is a flowchart of an intelligent process for controllingcomponents of an exemplary air handling system, in accordance with someembodiments of the disclosure.

FIG. 4B is a block diagram of an exemplary process of generating airquality prediction models, in accordance with some embodiments of thedisclosure.

FIG. 4C is a block diagram of an exemplary process of generating airhandling unit control instructions, in accordance with some embodimentsof the disclosure.

FIG. 5A is a block diagram of an exemplary process of training an airquality prediction model to optimize for temperature, in accordance withsome embodiments of the disclosure.

FIG. 5B is a block diagram of an exemplary process of applying an airquality prediction model to optimize for temperature, in accordance withsome embodiments of the disclosure.

In the figures, the left-most digit(s) of a reference number identifiesthe figure in which the reference number first appears. The use of thesame reference numbers in different figures indicates similar oridentical items or features. Moreover, multiple instances of the samepart are designated by a common prefix separated from the instancenumber by a dash. The drawings are not to scale.

DETAILED DESCRIPTION

The present disclosure generally pertains to control systems and methodsfor the modeling and management of air quality systems in industrialapplications. For example, the disclosure is directed to automatedsolutions for balancing diverse air quality devices through theapplication of machine learning technology.

In an exemplary embodiment, an indoor air quality control system may beimplemented to control a plurality of air handling and distributionunits within an industrial facility in a concerted effort to affect anoverall air quality goal. An implementation of the air quality controlsystem may include any of air handling units, air distribution units,baghouses, exhaust systems, sensor devices, site management devices,and/or supporting computing resources, and other physical and logicalcomponents relevant to implement air handling equipment in/for a singlefacility. In an exemplary embodiment, a plurality of sensors arepositioned to operate at different physical locations throughout (and insome embodiments both inside and outside) an enclosed facility such as abuilding or similar manufacturing location in which the air can becontrolled. The sensors variously measure environmental values relevantto air quality, such as humidity, temperature, air pressure, a level ofcontaminants, and the like. In some embodiments, the system may leverageexisting nodes and/or networked lighting systems to add additionalsensing capability or to add additional communication flexibility to themesh network. The sensors, together with one or more other devices, mayact as a wireless mesh network to transfer and receive data across thefacility. The sensors operate periodically to sense their respectiveenvironmental conditions, and the sensor data is sent, through the meshnetwork, to a gateway device. The gateway device may store the sensordata in a database, either local or remote to the gateway.

In some embodiments, one or more remote servers analyze the sensor data,historical data, and other environmental data (e.g., predicted weatherdata) in a dataset, and use one or more machine learning algorithms tomodel the air quality within the facility. In the exemplary embodiment,the sensor data is considered holistically (and sometimes in anaggregated or stratified manner), that is, the sensed air quality datais considered in a pervasive analysis of the overall condition of thefacility, rather than individual, independent analyses of data withrespect to the position of each sensor. As the sensors continue toprovide additional data (through periodic sensing), machine learningtechniques may be applied to progressively generate modified, improvedmodels and algorithms for decision making. The models predict thebehavior of the air in the facility given a variety of parametersindicating correlation between collected data and air quality, and areused to identify actions to take to meet desired air handling goals.Error minimization functions may be applied over the predictive modelsto adhere the outputs of the models to a desired optimization target (orone or more of several goals or desired outcomes), such as energy usage,particulate concentration, temperature, humidity, and/or pressure. Thedecisions regarding the limitations and/or goals of the model are donein accordance with various appropriate environmental health and safetyconsiderations, as defined by a site administrator, industry standard,and the like.

In some embodiments, information indicative of how temperature,pressure, humidity, contaminants, and other air conditions change at andthrough different points in the facility may be calculated and used asinput(s) to the machine learning analysis of the behavior of air in thefacility. A gradient from sensor to sensor and/or the flow ofparticulates through the facility may be determined based on themeasured readings at two or more sensors at known locations.Additionally, a rate of change at one or more sensors over a period oftime can be measured and calculated. Measurement of these changes (thatis, the delta value of change, a rate of change, or the like) can becomputed, e.g., by software, based on data from one or more differentsensor points into the facility, and the calculated changes fed into oneor more machine learning algorithms in addition to individual sensorreadings.

In the exemplary embodiment, the air handling models are applied to thecurrent sensor data to generate instructions, used by the gateway, tocontrol various air quality equipment in the facility to reach anoptimized result. For example, the instructions may be used toselectively turn components on or off, or more finely control those withvariable settings. Rather than evaluate the actions of each air handlingunit, MAU, baghouse, exhaust, lighting, and/or other equipment inisolation, the instructions are configured to individually calibrate theoperation of each of the multiple units to perform certain actions, theindividual actions taken by the unit having been calculated infurtherance of a holistic treatment of the facility. In an exemplaryembodiment, the control instructions generated based on the air handlingpredictive model (and passed through the mesh network of devices by thegateway) are comprehensive of all types of equipment, and acrossmultiple units and multiple types of units, functioning together toimprove air quality. As a result, for instance, a set of instructionsmay be passed to a first device to alter its activity based on senseddata from a second, differently-located device.

In conventional solutions directed to residential and commercialfacilities, the focus of an air handling system is typically on thecomfort of the occupants. A residential application may have a singleHVAC unit with an air handler, and any control of this unit to heat andcool is done to keep the unit functioning within a desired temperaturerange. A commercial application (such as an office or retail space) mayhave a centralized HVAC with a cooling or heating agent to push air outto any of many air handlers. However, as with a residential application,these air handlers are often identically controlled (multiple devicescontrolled as a single unit), and again, can be limited in scope toaccommodating human comfort. In conventional industrial applications,sensors (e.g., thermometers) controlling an HVAC unit are limited to adiscrete set of knowledge sensed by and relating to itself (the datacollected by the single sensor). Even if the actions of one HVAC unit(or MAU, baghouse, etc.) influences surrounding sensors, there is nocoordination of individual knowledge or action between different sensorsand attached air handling or distribution units.

In contrast, by virtue of the systems and methods described herein,intelligent control of unit-level activity is holistically appliedacross different types of air handling equipment to provide a moreefficient solution. Rather than controlling one HVAC or one system, theair handling systems described herein control many different types ofequipment to provide optimal air quality and environmental health.Because the learning algorithms described herein can be optimized forany of several different goals, facility-specific and industry-specificsets of rules can be sustained. Further, the application of machinelearning creates an air handling system that is not solely reactive tosensed immediate-need problems, but instead considers long-term goals(e.g., one or more hours ahead) to maintain and improve air quality in asustainable, energy-efficient way. In certain embodiments, for examplewhen used in a manufacturing facility, this may improve equipment andmanufacturing uptime. Accordingly, environmental health and safetyconditions can be met in an automated fashion, while reducing wastedenergy and labor resources.

Further, unlike conventional solutions that primarily optimize for humancomfort, the systems and methods described herein are directed to airhandling control in industrial facilities. As a result, the disclosedsystems focus on improved mechanisms for maintaining worker safetycompliance, maintaining environmental pollutant compliance, andmaintaining acceptable climate conditions for sensitive materials and/orequipment, these conditions being set by appropriate health and safetystandards specific to the purpose and needs of the facility, and inaccordance with industry standards.

Additionally, the functionality of the system and methods describedherein can be applied to existing industrial installations, and is notlimited to new construction or new installations of air handling devicesor networks. Therefore, even when the existing hardware in a facility isnot capable of performing a desired level of analysis or control, thesite control devices described herein can be configured to interfacewith existing hardware to implement control decisions made based onmodelled behavior. Also, additional pre-existing networked devices (suchas lighting nodes) may be used to facilitate transmission of airhandling instructions over a mesh network, simplifying and increasingthe reliability of networked communication. Because of this, facilitiescan reduce or avoid heavy time, money, and labor investment ininstalling updated hardware and software in large-scale facilities.

FIG. 1 depicts an exemplary air handling system 100, in accordance withsome embodiments of the present disclosure. As shown, a plurality ofsensors are positioned to operate at different physical locations withina facility 110. Facility 110 is, in the exemplary embodiment, amanufacturing location with a large open indoor space, but can be anyindustrial space of any shape or configuration that is not (or notcompletely) an open-air location, whether one or more multiple rooms, orany location in which air handling equipment can be controlled to affectthe environmental conditions. In the illustrated embodiment, a gateway150 (or other type of site controller) operates to communicate over awireless network with air-quality sensors 120, air handling anddistribution units (e.g., any combination of one or more of HVAC 130,MAU 135, baghouse 140, exhaust 145), a time-series database 160, and/ora remote server 170 (which may include one or more application servers).Gateway 150 may be configured to execute an air quality predictionalgorithm, developed at the server 170 using an environment modelinglogic 175 based on data from time-series database 160, data from one ormore third party databases 180, and/or other data from gateway 150, togenerate instructions for controlling the air handling and distributionunits 130, 135, 140, and 145. In another embodiment, the generation andtraining of the air quality prediction algorithm (which may include oneor more machine learning algorithms) is performed at the gateway 150;that is, the components and/or functionalities of the server 170discussed herein (or portions thereof) may be incorporated into theimplementation of the gateway 150.

A plurality of sensors deployed within the facility 110 each variouslymeasure environmental values within a respective sensor's range that arerelevant to air quality, such as humidity, temperature, air pressure, alevel of contaminants, and the like. In an exemplary embodiment, thesensors are sufficient in number and position to measure theseconditions across the entire facility. Wireless air quality sensors 120measure values such as temperature, humidity, pressure, VOC, carbonmonoxide, smoke, metal particulates, or other contaminants orpollutants. These air quality sensors may be placed in a 3-dimensionalgrid pattern throughout all or part of the facility, though anyappropriate configuration may be used given the size and shape of thefacility, and the placement need not conform to a gridlike or otherwiseregular pattern. While FIG. 1 only illustrates sensors 120 on a frontand back interior wall of a room (shown as surfaces S₁ and S₃, thesensors on these surfaces being shown in gray and white, respectively)for ease of illustration, such is merely exemplary and sensors 120 maybe positioned at any location, both internal and external to thefacility. Because the exemplary sensors are wireless, the positioning ofthese sensors is not limited to walls, ceilings, or floors, and indeed,such may be positioned at any appropriate point in a 3-dimensional space(e.g., on columns, furniture, underneath or within surfaces, and so on).Outdoor sensors may measure data such as air-pressure, temperature andhumidity. Electric current sensors 125 are placed on HVAC 130, MAU 135,baghouses 140, exhaust fans 145, de-stratification fans, other packagedunits (not specifically shown), or any other air handling, conditioning,or distribution units, to monitor the state of the respective units.Data is collected frequently from each of the sensors 120 and 125. Whileonly four air handling or distribution units and only 22 sensors areillustrated in FIG. 1 for ease of illustration, in practice the numberand variety of units and sensors may differ, given the size and needs ofthe facility. Additionally, not every facility may need every type ofsensor, and it may be generally understood that the systems describedherein can take in information from any non-zero number or type ofsensors and send control instructions to any number or type of airhandling, conditioning, or distribution units.

FIG. 2A depicts an exemplary embodiment of a site controller 200 formanaging a wireless network 220. The site controller 200 comprises anetwork management server 210 coupled to a gateway 150 (also referred toherein as an air handling control unit). In an exemplary embodiment,network management server 210 and gateway 150 are different logicalcomponents of site controller 200, such that the components may residewithin the same housing and/or share resources such as processinghardware, and in other embodiments they may be discrete network devicesconnected via a wireless or wired connection. In an alternateembodiment, the functionalities of network management server 210 andgateway 150 are all performed by a single logical component of thegateway 150. For ease of reference, the functionalities described hereinwith respect to gateway 150 may be understood to, in other embodiments,be implemented by different components of the site controller 200.

In some embodiments, the gateway 150 may receive messages from thewireless network 220 and encapsulate such messages in accordance withTCP/IP or other protocol for transmission of the messages to the networkmanagement server 210 through a WAN, LAN, or other type of network.Messages from the wireless network 220 to be transmitted to otherdestinations may be similarly encapsulated or otherwise converted into adifferent protocol as may be desired. In the opposite direction, thegateway 150 may de-encapsulate messages received from a WAN or othertype of network to remove overhead for routing messages. Gateway 150 mayalso generate messages (e.g., control messages) and transmit thosemessages to destinations on the wireless network 220. In addition to, oras an alternative to, wireless communication, any or all of thecomponents of the system 200 may be coupled to another component througha physical medium. In the present disclosure, for ease of discussion,the term “gateway” is disclosed as managing a “network,” however it isunderstood that the gateway may instead manage a portion of a network,or a subnetwork containing only a subset of devices of the entirewireless network. Additionally, while the term “gateway” is used, itwill be understood that such term can refer to any device that joins twonetworks (or subnetworks) so as to allow communication therebetween,even if the device performs other additional functions includingnon-network management functions. The gateway is understood to refer toeither hardware or software or any combination thereof.

In some embodiments, gateway 150 receives data from and transmits datato one or more of server 170 and databases 160 and/or 180. In someembodiments, server 170 and databases 160 and 180 may be local togateway 150 (e.g., within the facility or otherwise communicativelycoupled via local means), and in others, they may be located remotely,so as to be accessible through network 205. Network 205 may include, inthe exemplary embodiment, any type of (or any combination of one or moreof) wired network, such as Ethernet or fiber optics, wide area network(such as the Internet), local area network (such as an intranet),cellular network (e.g., Sprint, AT&T, or the like) or another type ofwireless network, such as Wi-Fi, Bluetooth, Bluetooth Low Energy, and/orother close-range wireless communications. In various embodiments,network 130 may be any IP-enabled network, including microwave, radio,and the like. In still other embodiments, one or more of any of theabove-listed network types may be used, or any combination thereof.

For illustrative purposes, it can be assumed that any of the componentsof FIG. 2A are capable of wireless communication with any device orcomponent connected to it (either directly or indirectly) by thedepicted lines. However, it will be noted that in addition to, or as analternative to, wireless communication, any or all of the components maybe coupled to another component through a physical medium.

As shown in FIG. 2B, wireless network 220 may be a network of devices,each illustrated as a node 225. Nodes 225 function, in an exemplaryembodiment, to implement an ad hoc mesh network. Each node 225 can bestationary (fixed in place within the facility) or can be mobile, suchthat it can be moved to a different physical location. In someembodiments, this network may be topologically variable, as mobile nodesmay move to a different location, additional nodes may be added, ornodes may become inaccessible. Different types of wireless or,alternatively, wired networks (or combinations thereof) are possible inother embodiments. In one exemplary embodiment, the nodes 225communicate among one another wirelessly, but it is possible for any ofthe nodes 225 to communicate with any of the other nodes 225 over aconductive medium. In other embodiments (not specifically shown), use ofa gateway may be unnecessary and gateway 150 may instead have acommunication device, such as an RF radio, that permits the networkmanagement server 210 to communicate directly with the wireless network220 in accordance with the protocol used by the wireless network 220. Ingeneral, gateway 150 uses multicast messaging to communicate with nodes225 across the network 220, such that messages may be sent to multiplenodes from a first transmitting node. In other embodiments, unicastmessages, broadcast messages, and/or other types of communication may beused.

Nodes 225 may include any variable number or type of devices. In theexemplary embodiment, a node 225 may be any of the sensors or devicesdescribed above with reference to FIG. 1, e.g., air-quality sensors 120,electric current sensors 125, other types of sensors, HVACs 130, MAUs135, baghouses 140, exhaust fans 145, de-stratification fans, packagedunits or any other key air handling, conditioning, or distributionunits. In some embodiments, lighting systems installed in the facilitymay additionally or alternately act as networked nodes 225. Exemplarylighting systems are described in commonly-assigned U.S. Pat. No.9,374,874, entitled “Lighting Control Systems and Methods” and issued onJun. 21, 2016, which is incorporated herein by reference. In still otherembodiments, a node 225 may be a sensor or air handling device withlighting components. The particular configuration of the network andtypes of nodes on the network depend of course on the type, size, andconfiguration of the sensors and devices in the facility.

FIG. 2C depicts exemplary components of some of the possible nodes on anetwork 220, specifically, an embodiment where an exemplary each of anair quality sensor 120, HVAC unit 130, MAU (or other air handling unit(AHU)) 135, baghouse (or other filtering unit) 140, exhaust unit 145,and lighting node 290 function as a network node 225 that communicatesover the wireless network 120. While only one of each type of node isshown, this is merely exemplary and other embodiments may include anynumber of the various types of nodes (or a subset of the various types)and any number of total nodes. Additionally, while certain componentparts of each type of node are mentioned herein or illustrated, thenodes or devices are not so limited, and any known feature relevant tothe functioning of the device may be included in the variousembodiments, including add-ons or interfaces that are not strictlynecessary for air handling functions.

An exemplary air quality sensor 120 may comprise one or more sensors 242(measuring any relevant environmental value(s) within the sensor's rangethat are relevant to air quality, such as humidity, temperature, airpressure, a level of contaminants, and the like). The sensor 120 mayalso include a controller 244 for controlling the sensor (e.g., turningit on/off), a relay 243 configured to regulate the supply of electricalcurrent to the sensor 242 based on control signals from the controller244, and a network interface 246 for communicating via wireless (e.g.,RF) signals comprising a transmitter (TX) 246-A for transmitting awireless signal and a receiver (RX) 246-B for receiving a wirelesssignal.

An exemplary HVAC unit 130 may comprise one or more thermostats 252(measuring temperature and the like), a controller 254 for controllingthe thermostat (e.g., turning it on/off, changing the heating/coolingsetpoints, and/or changing the fan mode), a relay or digital bus 253configured to change thermostat settings and read data from thethermostat 252, based on control signals from the controller 254, and anetwork interface 256 for communicating via wireless (e.g., RF) signalscomprising a transmitter (TX) 256-A for transmitting a wireless signaland a receiver (RX) 256-B for receiving a wireless signal. HVAC 130 mayalso include one or more other system components 257 that may (or maynot) be controlled based on control signals from the controller 254,such as a heat exchanger, blower/fan, condenser, evaporator coil,combustion chamber, compressor, or any other controllable element.

An exemplary MAU 135 or other air handling unit (AHU) may comprise oneor more fans or blowers 262, a controller 264 for controlling thefans/blowers (e.g., turning on/off or changing to any of a variablespeed if appropriate), a relay 263 configured to regulate the supply ofelectrical current to the fans/blowers 262 based on control signals fromthe controller 264, and a network interface 266 for communicating viawireless (e.g., RF) signals comprising a transmitter (TX) 266-A fortransmitting a wireless signal and a receiver (RX) 266-B for receiving awireless signal. MAU 135 may also include one or more other systemcomponents 267 that may (or may not) be controlled based on controlsignals from the controller 264, such as heating coils, cooling coils,filters, humidifiers, or any other controllable element.

An exemplary baghouse 140 or other filtering unit may comprise one ormore fans or blowers 272, a controller 274 for controlling thefans/blowers (e.g., turning on/off or changing to any of a variablespeed if appropriate), a relay 273 configured to regulate the supply ofelectrical current to the fans/blowers 272 based on control signals fromthe controller 274, and a network interface 276 for communicating viawireless (e.g., RF) signals comprising a transmitter (TX) 276-A fortransmitting a wireless signal and a receiver (RX) 276-B for receiving awireless signal. Baghouse 140 may also include one or more other systemcomponents 277 that may (or may not) be controllable based on controlsignals from the controller 274, such as bags, tubes, etc., for example,compressed air to pulse the bags on a certain frequency.

An exemplary exhaust unit 145 or other ventilating unit may comprise oneor more fans or blowers 282, a controller 284 for controlling thefans/blowers (e.g., turning on/off or changing to any of a variablespeed if appropriate), a relay 283 configured to regulate the supply ofelectrical current to the fans/blowers 282 based on control signals fromthe controller 284, and a network interface 286 for communicating viawireless (e.g., RF) signals comprising a transmitter (TX) 286-A fortransmitting a wireless signal and a receiver (RX) 286-B for receiving awireless signal. Exhaust unit 145 may also include one or more othersystem components 287 that may (or may not) be controllable based oncontrol signals from the controller 274, such as ducts, etc.

An exemplary lighting node 290 may comprise a light source 292 such as alight emitting diode (LED) (or if appropriate, a laser diode, afluorescent lamp, an incandescent light, or other light source) mountedin a lighting fixture, a lighting controller 294 for controlling thelight source 292, a relay 293 configured to regulate the supply ofelectrical current to the light source 292 based on control signals fromthe lighting controller 294, and a network interface 296 forcommunicating via wireless (e.g., RF) signals. Network interface 296 maycomprise a transmitter (TX) 296-A for transmitting a wireless signal anda receiver (RX) 296-B for receiving a wireless signal.

In some embodiments, each of (or a subset of) network interfaces 246,256, 266, 276, 286, and/or 296 may use wireless (e.g., via radiofrequency (RF)) communication to transmit/receive data to and from othernodes, other devices (e.g., assets or tags) on the network, and/or thegateway 150. In other embodiments, these devices could alternatelycommunicate with infrared or ultrasound technology, or any otherappropriate type of wireless communication. Further, each node mayinclude one more memories (illustrated as elements 245, 255, 265, 275,285, and 295). These memories may comprise any suitable storage medium,either volatile and non-volatile (e.g., RAM, ROM, EPROM, EEPROM, SRAM,flash memory, disks or optical storage, magnetic storage, or any othertangible or non-transitory medium), that stores information that isaccessible by a processor. The nodes may also include one or moreprocessing element(s) (not specifically shown, such as one or morecentral processing units (CPU), digital signal processors, otherspecialized processors or combination of processors,application-specific integrated circuits (ASICs), field-programmablegate arrays (FPGAs), microprocessors programmed with software orfirmware, and/or any other circuitry that communicates to and drives theother elements within the node.

FIG. 3A depicts an example schematic diagram of components of a gateway150 in accordance with an exemplary embodiment of the presentdisclosure. FIG. 3B depicts an example schematic diagram of componentsof a remote server 170 in accordance with an exemplary embodiment of thepresent disclosure. While FIGS. 3A and 3B illustrate certain respectiveconfigurations of components, it can be understood that any practicalconfiguration may be used, and the components need not fall into theparticular logical groupings illustrated in FIGS. 3A and 3B. Further, itwill be generally understood that the architectures described below andillustrated in the figures are not limited to the components discussedherein, and may include other hardware and software components. Rather,for ease of illustration, only the components and functionalities mostrelevant to the subject systems and methods are discussed herein.

The exemplary gateway 150 comprises at least one processor 310, such asone or more central processing units (CPU), digital signal processors(DSP), graphics processing units (GPU), application-specific integratedcircuits (ASICs), field-programmable gate arrays (FPGAs), and/ormicroprocessors programmed with software or firmware, other specializedprocessor or combination of processors, or other circuitry thatcommunicates to and drives the other elements within gateway 150 via alocal interface 330, which may include at least one communication bus.In some embodiments, the processor 310 may comprise, in whole or inpart, an artificial neural network or other type of configuration forperforming machine learning functions based on instructions stored inmemory 340.

The gateway 150 has a network interface 320 for enabling communication(directly or indirectly) with other devices, including the nodes 225 andother objects capable of RF communication such as RF-capable tags. In anexemplary embodiment, network interface 320 enables the gateway 150 tocommunicate with network management server 210 and/or in someembodiments, remote devices such as server 170, such communicationtypically being performed over a wide area network (WAN), such as, forexample, the internet, local network(s), or other type of network. In anexemplary embodiment, the network interface 320 is configured tocommunicate wirelessly over one or more geographic areas, but theinterface 320 may alternately or additionally facilitate exchange ofdata via a physical medium.

The gateway 150 has a memory 340 that stores a control logic 350 forgenerally controlling the operation of the gateway. The control logic350 may also be configured to, via the network interface 320,communicate with the nodes 225 by transmitting messages to or receivingmessages from their respective network interfaces 246, 256, 266, 276,286, 296 in order to control their operation, for example, to obtainsensor or status information from the nodes, push instructions, or thelike. For example, where a node 225 is a HVAC unit 130, the controllogic 350 may communicate with a control interface of the HVAC 130 tomanage an on/off state or to send instructions with planed or scheduledon/off periods, temperatures and/or speeds at which to operate, or thelike. The control logic 350 can be implemented in software, hardware,firmware, or any combination thereof.

Memory 340 may also store a scheduling logic 352 and/or an exceptionhandling logic 354 for determining instructions for controllingoperation of one or more networked nodes 225 within an RF range of thenetwork. In some embodiments, the functions of scheduling logic 352 orexception handling logic 354 (or a subset of either) may be performed bythe control logic 350. Memory 340 may also store a database 360comprising sensor data 362 (which in some embodiments may includeaggregated, manipulated, or otherwise processed sensor data), controlinstruction data 364 (which may include instructions or schedulesrelating to the nodes of network 220), health and safety data 366 (whichmay include data regarding one or more environmental, health and safetystandards or regulations), and/or model stage data 368. While a singledatabase 360 is depicted in FIG. 3A, some embodiments may instead useone or more databases or may use a database shared by other componentsseparate from the gateway 150. Further, while FIG. 3A refers only asingle “map”, “table”, or “database,” it will be understood that thesecomponents are not so limited nor is any particular form orconfiguration of data storage mandated, and the described “databases”may alternatively be indexed tables, keyed mappings, or any otherappropriate data structure(s). Various other data and code can also bewritten to or read from memory 340.

As used herein, memory 340 may refer to any suitable storage medium,either volatile and non-volatile (e.g., RAM, ROM, EPROM, EEPROM, SRAM,flash memory, disks or optical storage, magnetic storage, or any othertangible or non-transitory medium), that stores information that isaccessible by a processor. While FIG. 3A illustrates a single discretememory 340, it will be understood that the embodiments described hereinare not limited to any particular arrangement and that other embodimentsmay store information in one combined memory, or with information storedin a different configuration in one or more memories, some local to theother components illustrated in FIG. 3A and/or some shared with, orgeographically located near, other remote computing systems.

Control logic 350, scheduling logic 352, and/or exception handling logic354 may be implemented in software (as in FIG. 3A), hardware, firmware,or any combination thereof. In some embodiments, scheduling logic 352(and in some embodiments control logic 350 and/or exception handlinglogic 354) may be implemented in whole or in part as a machine learningsystem (e.g., neural network software) for achieving the functionalitiesdescribed herein, for example, using one or more machine learningalgorithms, as described in more detail below. When the machine learningsystem is implemented in hardware, it may be integrated into a hardwarechip to form a part of the processor 310. For example, the machinelearning system may be manufactured in the form of a dedicated hardwarechip for artificial intelligence, or may be manufactured as part of aconventional general purpose processor (e.g., a CPU or an applicationprocessor) or a graphics dedicated processor (e.g., a GPU). Controllogic 350, scheduling logic 352, and/or exception handling logic 354,when implemented in software, can also be stored on anycomputer-readable medium, for example electronic, magnetic, or opticalmediums, among others, or another system, apparatus, or device. In theembodiment of FIG. 3A, logics 350, 352, and 354 are implemented by theprocessor 310 or may in other embodiments be implemented by any othercircuitry capable of executing the instructions thereof.

FIG. 4A depicts an exemplary process 400 of taking in data from sensorspositioned within and/or outside a facility, intelligently analyzingthat data to train a machine learning model to predict an overallcondition of the air within the facility, generating air-handlinginstructions using the model that optimize for a defined air handlinggoal, and controlling the operations of various types of air qualityequipment within the facility based on those instructions. The process400 is repeated periodically as the sensors collect additional data, andthe models are continuously retrained with the benefit of the additionaldata.

With reference to FIG. 1, a plurality of sensors 120, 125 are configuredto periodically measure conditions relating to an air quality of asensed area. At step 402, the sensors collect this data and transmit thedata to the gateway 150 via the mesh network 220. Sensors within thefacility will collect data on indoor ambient conditions, while sensorson the outside of the facility (e.g., on the roof or outside walls orpositioned farther away from the building) collect data on outdoorambient conditions. These conditions may variously include, forinstance, temperature, air density, air pressure, a level ofcontaminants (for example, targeted measurements of carbon monoxide,ozone, particulate matter (dust), sulfur dioxide, nitrogen dioxide,carbon monoxide, lead, welding fumes, oxygen, other toxic or non-toxicair pollutants) or any other appropriate condition. The specificcontaminants measured may depend on the type of sensors, the type offacility, the predefined instructions of a site administrator, and, inthe exemplary embodiment, the health and safety requirements and/orenvironmental requirements that apply to the industrial facility. In anexemplary embodiment, sensors 120, 125 are each respectively dedicatedto the collection of one type of data (for example, in the interest ofspeed of collection, energy use, reducing uptime, or the like), howeverin other embodiments, sensors may exist that collect one or more of avariety of data types or measurements. The data collection occurs basedon the air quality at the individual physical position of a sensor andthe data is transmitted, by the sensor via multicast messaging, to thegateway 150 where it is stored as sensor data 362.

In the exemplary embodiment, sensor data is collected periodically,e.g., every 15 minutes or another other appropriate amount of time, witheach of the sensors collecting data along the same schedule. In otherembodiments, each sensor may collect data periodically (e.g., every 15minutes) but on its own, independently managed schedule. In still otherembodiments, a subset of sensors may operate on a different schedule ormay not function periodically. For example, some sensors may insteadadhere to a set sensing schedule or may perform a sensing operation uponthe initiation of a trigger, such as the occurrence of an event, thereceipt of a control instruction (from gateway 150, another networkednode, a remote device, from within the node 225 containing the sensor,upon an manual instruction (e.g., the push of a button by a siteadministrator). As just one example, a sensor may not be configured tosense periodically, but may be triggered to perform a sensing operationby an alert or instruction sent from a networked device. As anotherexample, a sensor may periodically perform one type of sensing (e.g.,temperature sensing), and may, upon the occurrence of an instruction orevent, additionally perform one or more other types of sensing (e.g.,air pressure or contaminant sensing). Each sensor may transmit itssensed data to the gateway in accordance with a timestamp or othertime-indicating data.

Once sensor data has been collected, the process advances to step 404,at which the gateway transmits sensor data 362 (and in some embodiments,health and safety data 366) to a remote database such as time-seriesdatabase 160. In one embodiment, gateway 150 may refer to controlinstruction data 364 or other configuration data stored in database 360to determine which sensors are configured to transmit sensor results ata given time, and may only proceed to analyze the data if all data hasbeen received. If data from one or more sensors has not been received,the gateway 150 may query those non-transmitting sensors as to theirstatus. In another embodiment, gateway 150 may wait a predeterminedperiod of time from the last (previous) data transmission to receive anytransmitted data from the sensors and, upon expiration of that time, mayassume that all data has been received. In some embodiments, gateway 150may, in step 404, transmit sensor data to time-series database 160 uponreceipt of all the data or on a rolling basis as sensor data isreceived, e.g., in real-time or near real-time immediately upon receipt.In an exemplary embodiment, data is received or posted by the gateway150 via one or more APIs to/from the relevant source/destination device.In some embodiments, the gateway 150 may proceed to step 404 even if itis aware that not all expected sensor data has been received, in theinterest of timely control of the facility equipment.

The process then proceeds to step 406, in which the gateway determines,based on the collected sensor data both at the gateway and in thetime-series database 160, whether the air conditions in the facility areoutside the tolerances of the predictive model (described below). Thatis, exception handling logic 354 may be configured to recognize theoccurrence of an unpredicted event, a dangerous condition, a lack ofdata, an unusually high or low sensed value or an otherwise unexpectedcondition. In an exemplary embodiment, the boundaries of “normal” oracceptable facility conditions are not set by human comfort, but rather,are bounded by environmental health and safety standards. In anotherembodiment, human comfort or livable conditions (e.g., an acceptablecomfortable temperature range for humans) may be a secondary factor usedin the analysis, while the environmental health and safety of thefacility is a primary factor, and therefore a higher priority (or ahigher weighted importance) than human comfort. Unlike residential orcommercial applications in which not many hazardous conditions mayexist, industrial applications may quickly become unsafe if the facilityconditions are not optimized to capture and resolve airborne conditions.Accordingly, the predictive model initiates actions in response tosensing conditions of a predicted norm (e.g., alerts, notifications,escalations), and takes proactive steps in controlling air handlingunits to stop or mitigate the recognized occurrence of an eventviolating environment health and safety standards.

The normal operation of a facility may be designed to optimize forcertain conditions, such as energy savings. As no immediate dangerexists in normal operation, the compute time expended by the real-timeexecution of the predictive model (in the order of seconds or minutes)may be an acceptable delay with which to control or make changes to thefacility. However, outside the norm of operation, for some types ofexceptions (to an acceptable range of operations), the conditions (e.g.,the presence of a high level of contaminants or a high temperature) maybe so urgent as to prompt the gateway 150 to take a predefined action toresolve the condition rather than apply a predictive model, as in step406. In such a scenario, the process proceeds to step 420, in which thegateway 150 executes exception handling logic 354, a set of rules-basedlogic that sits atop the normal predictive model process. If insteadconditions are within a tolerance of the predictive model (step 408),the gateway continues to run the predictive model.

Step 420 defines a set of “exceptional” actions, where if sensormeasurements depart from environmental health and safety tolerances, animmediate action, such as an error, alert, signal or siren may be madeby the gateway device, by an alarm system (not specificallyillustrated), and/or one or more individual air handling units. In oneexample, gateway 150 may transmit an instruction to a facilitiesincident management response system (not specifically illustrated), suchthat a site administer is sent a notification such as a text message,with a preset error message, a report or limited character string withthe exceptional condition (e.g., CO alert) or sensor measurement (ordelta measurement from an earlier sensed value). In one example, gateway150 may additionally or alternately trigger emergency actions by one ormore of a MAU or baghouse 225 (or all such units, to push or pull airinto or out of the facility), and/or trigger sprinkler systems, alarms,or other emergency systems (e.g., emergency lighting systems). In someembodiments, the initiation of emergency actions based on the sensedvalues by one or more sensors in a certain geographic location wouldautomatically trigger gateway 150 to activate (or deactivate) airhandling units within that geographic location (or throughout thefacility), e.g., triggering the turning on (or turning up) of allremaining area baghouses, exhausts, and so on. That is, rather than asimple notification, gateway 150 implements its exception handling logicto trigger a responsive physical action within the facility in theinterest of environmental safety and health and to control the facilityback to acceptable environmental standards. In the case that thedeviation from the norm is relatively small, or may not arise to anemergency condition, gateway 150 can use the air handling units 225 tophysical manipulate the environmental conditions back within acceptablebounds faster than a site administrator could be notified, arrive, andassess the situation, and faster than the facility could otherwise beshut down or evacuated.

In steps 408 and 410, a predictive model is generated by the environmentmodeling logic 175 of server 170 to model the behavior of air in thefacility. The relevant functionality of the server 170 and theassociated hardware and software components are described herein withreference to FIG. 3B. The exemplary server 170 comprises at least oneprocessor 360 such as one or more central processing units (CPU),digital signal processors (DSP), graphics processing units (GPU),application-specific integrated circuits (ASICs), field-programmablegate arrays (FPGAs), and/or microprocessors programmed with software orfirmware, other specialized processor or combination of processors, orother circuitry that communicates to and drives the other elementswithin server 170 via a local interface 380, which may include at leastone communication bus. In some embodiments, the processor 360 maycomprise, in whole or in part, an artificial neural network or othertype of configuration for performing machine learning functions based oninstructions stored in memory 390.

According to an exemplary embodiment of the present disclosure, theserver 170 has a memory 390 that stores an environmental modeling logic175. Environmental modeling logic 175 may execute machine learningsolutions to model the environment (that is, the air quality) of thefacility 110 by analyzing sensor data collected from the nodes 225,historical data stored in time-series database 160, weather data, and/orother obtained data and determining which of those data (or combinationsof data) correlate with the facility's behavior. Memory 390 may storethe dataset(s) used for the machine learning as training data 394.According to an exemplary embodiment of the present disclosure, themachine learning model stored in the model storage 392 may includepredictive model 392-1.

While a single memory 390 and a single model storage 172 is depicted inFIG. 3B, some embodiments may instead use one or more databases or mayuse a database shared by other components separate from the server 170.Further, while FIG. 3B refers only a single “map”, “table”, or“database,” it will be understood that these components are not solimited nor is any particular form or configuration of data storagemandated, and the described “databases” may alternatively be indexedtables, keyed mappings, or any other appropriate data structure(s).Various other data and code can also be written to or read from memory390. The server 170 also has a network interface 370 for enablingcommunication (directly or indirectly) with other devices, including thegateway 150. As used herein, memory 390 may refer to any suitablestorage medium, either volatile and non-volatile (e.g., RAM, ROM, EPROM,EEPROM, SRAM, flash memory, disks or optical storage, magnetic storage,or any other tangible or non-transitory medium), that stores informationthat is accessible by a processor. While FIG. 3B illustrates a singlediscrete memory 390, it will be understood that the embodimentsdescribed herein are not limited to any particular arrangement and thatother embodiments may store information in one combined memory, or withinformation stored in a different configuration in one or more memories,some local to the other components illustrated in FIG. 3B and/or someshared with, or geographically located near, other remote computingsystems.

The environmental modeling logic 175 can be implemented in software (asin FIG. 3B), hardware, firmware, or any combination thereof. In someembodiments, environmental modeling logic 175 may be implemented inwhole or in part as a machine learning system (e.g., neural networksoftware) for achieving the functionalities described herein, forexample, using one or more machine learning algorithms, as described inmore detail below. When the machine learning system is implemented inhardware, it may be integrated into a hardware chip to form a part ofthe processor 360. For example, the machine learning system may bemanufactured in the form of a dedicated hardware chip for artificialintelligence, or may be manufactured as part of a conventional generalpurpose processor (e.g., a CPU or an application processor) or agraphics dedicated processor (e.g., a GPU). When implemented insoftware, environmental modeling logic 175 can also be stored on anycomputer-readable medium, for example electronic, magnetic, or opticalmediums, among others, or another system, apparatus, or device. In theembodiment of FIG. 3B, environmental modeling logic 175 is implementedby the processor 360 or may in other embodiments be implemented by anyother circuitry capable of executing the instructions thereof.

As described herein, environmental modeling logic 175 is executed byprocessor 360 to model the behavior of air in the facility and predictthe condition of the air within a given amount of time. In a case thatthe air quality deviates to an undesired degree from a desired airquality or facility goals (e.g., temperature, energy use, or the like),or any environmental health and safety standards are not met orexceeded, environmental modeling logic 175 generate air-handlinginstructions (e.g., functional prompts or commands) to be executed bythe individuals air handling units 225 to bring the air conditionswithin acceptable thresholds. In alternate embodiments, some or all ofthese functionalities, including the generating, training, and/orexecution of a predictive algorithm and/or the generation ofinstructions for individual air handling units 225, may be performed ator by the gateway 150 or another component within environment 100.However, in an exemplary embodiment, at least the training of one ormore predictive models based on collected sensor data is performed atthe remote server 170, that is, in the cloud, so as to offload the morecompute-intensive activity away from the gateway 150. Otherconfigurations may be variously used as permitted by the computationaland resource limitations of the components of environment 100.

The generation of a predictive model relies upon a machine learningsolution trained on the set of sensor data in time-series database 160,obtained from gateway 150, and/or obtained from one or more third partydata repositories 180. This data may include the sampled air data fromthe various networked sensors, either received by the gateway 150 andtransmitted directly to the server 170 or retrieved from the time-seriesdatabase 160. Database 160 may store data collected in real-time fromthe gateway 150, including the most recent sensor data, as well ashistorical data from previous samples of sensed data. Historical datamay be aggregated or sorted by, for example, timestamp or time period.In an exemplary embodiment, historical data is stored indefinitely,however, in other embodiments, available sensor data may be limited forcomputational or storage efficiency. For example, embodiments may existwhere database 160 stores only the most recent data (e.g., the past xday(s), month(s), past year(s), etc.) and discards older data as beingless relevant or less reflective of current conditions. In someembodiments, where sensor data from the facility is limited (forexample, in a new installation), database 160 may be pre-populated with(or may obtain upon the instruction of a site administrator) data fromcomparable facilities, as selected by the site administer based onsimilarity to the facility 110, e.g., similar industry, size, devicetype and/or number, environmental hazards or conditions, regulatoryrequirements, weather conditions, or the like. In the exemplaryembodiment, the trained model is intended to match the behavior of theparticular profiled facility, and therefore, comparable facility datamust be selected carefully, if used at all.

Environment modeling logic 175 may further obtain data from one or morethird-party servers 180. In one embodiment, this third-party data mayinclude a weather prediction or analysis, such as a determination ofpredicted atmospheric conditions, outside temperature, pollutionforecast. As one example, environmental modeling logic 175 may, at thetime of training, on a periodic basis, or in real-time when executing astored model 392-1, may request weather forecast data from a third-partyweather service. This forecast data may be limited in time to, e.g., thenext one, six hours, or any appropriate value typically corresponding tothe period of time over which the model intends to predict thefacility's behavior. In particular, the data may be used by server 170and/or gateway 150 to determine the impact of feeding outside air intothe facility by the local air handling units. As just one example, wherethe pollutant prediction of the outside air indicates unfavorableconditions, such data may indicate that importing outside air into thefacility would negatively impact air quality, and would recommendfavoring a filtering, recycled air, or another alternative process.

In some embodiments, environmental modeling logic 175 may leverage othernon-air handling devices on the wireless network 220 to obtainadditional information about the overall behavior of the facility. Forinstance, one or more nodes of the wireless network 220 may be lightingsystems 290 capable of communicating with the gateway 150, via RFcommunication, about lighting conditions or other environmentalconditions at the physical position of the lighting system. Somelighting systems 290 may be integral with or coupled to sensors capableof sensing values relevant to air quality, humidity, temperature, energyusage, particulate concentration, air pressure or the like, and suchsensor readings may additional inform the real-time and historical dataused in a machine learning analysis. As just one example, informationfrom lighting systems 290 may inform the overall energy usage of thefacility, both by the energy consumption of the lighting systemsthemselves, as well as the scheduling/modes of the lighting systems,which may provide information regarding the occupancy and use of thefacilitate at different days and times.

A goal of the environment modeling logic 175 is to generate a predictivemodel that optimizes for a given air quality goal that can be metthrough control of the facility's air handling and distribution units.To this end, in step 408, one or more logics at the server 170 identifyat least one desired outcome for the air in the facility. As an example,a desired outcome may include any of energy usage, particulateconcentration, temperature, humidity, and/or air pressure, however, inthe exemplary embodiment, the particular desired outcome will bedetermined by a site administrator, for example based on the facilityneeds (e.g., cost) and/or environmental health and safety regulatoryrequirements. In some embodiments, the real-time sensor data mayindicate that one or more conditions is occurring that should be givenprecedence over any other routinely-desired system goals. That is, wherethe real-time sensor data suggests that the circumstances areapproaching (or have reached) values outside of an expected oracceptable range, such as rising/falling temperature, equipment failure,rising energy usage, unsafe particulate or toxicity conditions (whetherfor human or machine operation), or the like, then in some embodimentsthe mitigation of the abnormal condition (a conditionally-desired goal)may be taken automatically as the desired outcome or a primary outcome,overriding a predetermined goal input by a site administrator.

In step 410, the collected and aggregated data is used by environmentmodeling logic 175 to generate training data with which to train an airquality predictive model. The environment modeling logic 175 analyzeshistorical and real-time sensor data (and/or other third party data,e.g., weather data) as training data to generate a predictive model ofthe behavior of air within the facility over a particular period of time(one hour or the like). This training data may be stored in one or moretraining databases 394 at the server 170.

Training data may comprise, as one example, a set of pairs or tupleseach comprising a collection of sensor and condition measurements thatserve as input to the predictive model, though other embodiments maydiffer. The sensor and condition measurements may include any of actualvalues obtained by the sensors, deltas or changes in sensed values,sensor values aggregated by time, location within the facility, networkgroup or subgroup, sensor type (e.g., temperature, pressure, humidity,CO2, contaminant levels, and so on), sensor purpose (e.g.,standard/emergency or other grouping), and/or any other appropriate typeof delineation or classification of sensor data. In some embodiments,the generation of the tuples is part of a supervised learning process,for example, the parameters and/or weights of the training set may beselected by a network or site administrator. In other embodiments thetraining may be part of an unsupervised learning process, whereenvironmental modeling logic 175 applies one or more machine learningmodels to find undetected patterns or correlations between the sensorvalues in the received data set and the overall behavior of the air inthe facility (which act as parameters to the model) without pre-existingdata labels or features and with a minimum of human supervision. The setof tuples in the dataset can be modified over time to reflect feedbackfrom site/network administrators regarding whether the output(s) metsafety and environmental health optimization targets and weresufficiently predictive or accurate, as well as the feedback regardingthe practicality of the output(s) against the operation of the facility.

Machine-learning is applied to every air-quality sensor to build a modelof correlation between the air at that point in the facility, therespective states of the various air-handlers (e.g., HVAC, MAU,baghouses, etc.), and/or the indoor average ambient conditions andoutdoor ambient conditions. The environment modeling logic 175 works bytaking in all of this information, looking across the entire facility,and determining optimal conditions for every HVAC, MAU, exhaust,baghouse, and other air handling components in order to adhere to theoptimal air quality goals across the facility generally. The trainedpredictive model is stored in model storage 392 as predictive model392-1.

FIG. 4B illustrates an exemplary data flow through environment modelinglogic 175. Data from any of N air-handler electrical current sensors 125and/or air quality sensors 120 (shown as air-handler electricalcurrent_(0-N) 125-A, air quality sensor_(0-N) 120-A, and air qualitysensor_(0-N) 120-B) is taken together as input to a supervised learningmodule 440 for the air quality sensors. More specifically, data from anyof N air-handler electrical current sensors 125 may be used by statedetector module 432 to obtain information about the state of any of thenodes 225 on the network (e.g., their on/off state, their speed, theiroperating temperature, and the like). Data from any of M air qualitysensors 120 located inside the facility may be used by indoor ambientcondition pre-processing module 434, and data from any of the P airquality sensors 120 located outside the facility may be used by outdoorambient condition pre-processing module 436. Modules 432, 434, and 436process and conform the data from the respective inputs 125-A, 120-A,and 120-B for use by the supervised learning module 440. Data for aparticular air quality sensor i (air quality sensor_(i) 438) is takeninto the supervised learning module as a dependent variable.

Supervised learning module 440 uses one or more learning algorithms togenerate an air quality prediction model 445 for sensor_(i). While theillustrated module is labelled as a supervised learning mechanism, anyappropriate machine learning system can be used by the environmentmodeling logic 175 in different embodiments, such as supervised learningsystems (e.g., regression trees, random forests), unsupervised learningsystems (e.g., k-means clustering), support vector machines, kernelmethod, and Bayesian networks (probabilistic directed acyclic graphicmodel). In an exemplary embodiment, a Support Vector Regression (SVR)supervised learning algorithm with a polynomial (poly) kernel functionis used to train the model against the set of training data. In otherexemplary embodiments, other types of multiple regression analysis(e.g., SVR with the Radial Basis Function (RBF) kernel, linear kernel,or sigmoid kernel, or alternatively, simple regression, multipleregression, polynomial regression, linear regression) may be used.

The generation of the air quality prediction model involves theidentification of one or more air quality prediction model parameters,each corresponding to the correlation between the air quality and thesensor data, and may include at least one of a weight or a bias againstthose parameters. In that regard, environment modeling logic 175 maytrain the prediction model by adjusting a weight (and, if necessary, abias value) of the data from various air handling nodes (and othersources/types of data) to obtain a desired output for a given input.With reference to FIG. 4B, every relevant sensor value in the trainingdata 125-A, 120-A, and 120-B can be multiplied by a corresponding weight(a dimensional constant) that captures its effect on the overall airquality of the facility (the weights being discretely assigned as any ofa set of positive or negative values), and summed together to form anoutput. These assigned weight values may be consistently updated throughthe learning process as the model is trained and retrained. Further, themodel may be trained using a method such as back propagation. Thetrained (or re-trained) air quality prediction model 445 is stored inmodel storage 392 (predictive model 392-1).

In an exemplary embodiment, the data used by environment modeling logic175 to train model 392-1 may be routinely obtained or cached or stored(as training data 394) so as to be available offline (without networkinteraction) to environment modeling logic 175 such that environmentmodeling logic 175 can re-run the model on a periodic or set basis. Themodel is frequently updated as new data is collected in order tomaintain the integrity of the model. This revision of the parameters isrepeated iteratively with each subsequent data push from the sensors tothe gateway 150, until the parameters of the model are highly accurate.In an exemplary embodiment, this re-running occurs every 15 minutes oron the push of new sensor data to the gateway 150, however otherembodiments may differ so as to re-run the model, e.g., every minute,hourly, daily, weekly, monthly, upon request or according to a schedule,and/or on a real-time basis. It will be generally understood thatre-running model 392-1 is resource dependent, and therefore thefrequency of update may be based on the needs of the facility andavailable computing resources. With this model, the behavior of thefacility under external forces and various air-handler states can besimulated.

In an alternate embodiment, the training of the model may be run (orre-run) only when instructed, such that a manual command or occurrenceof an event may trigger a retraining. In one example, the control logic350 may determine that sensor data 362 suggests that a desired orexpected conditional threshold has been exceed (e.g., the temperaturesensed by one or more sensors is abnormally high), and there has been anequipment failure or multiple failures, that an insufficient amount oftraining data exists relevant to the overall sensed conditions of thefacility, or any appropriate trigger. In one embodiment, the retrainingis conducted with a different set of target values in mind, for example,being more flexible on, or removing dependency of the model on, one ormore desired outcomes (as one example, increasing the range ofacceptable energy usage) thereby allowing a greater set of actions to betaken while still being within target air quality bounds. Put anotherway, the model may be retrained with certain default or best practicesso as to be less aggressive on certain target conditions, in the casethat the sensor data 362 suggests that certain predetermined scenarios(e.g., temperature or contaminant levels) have occurred or are beingapproached. These predetermined scenarios may be selected by a networkor site administrator, and may in some instances rely on data-specificconditions. As just one example, if the historical amount of sensed dataavailable is less than a predetermined amount (e.g., two weeks of data)the model may be programmed to meet more conservative conditions underthe assumption that the system 100 does not have enough information toreliably predict outlier responses in less-known scenarios.

Model 392-1 is applied in an attempt to optimize for an objectivefunction 452 defined by a desired air handling outcome. Morespecifically, in step 410 (FIG. 4A), the prediction model 392-1 (havingbeen trained on historical data) is now run using the most current (thatis, subsequently received) sensed data (including, e.g., air qualitysensor data 454 and/or ambient weather data 456), applying errorminimization based on the previously-set desired outcomes for air in thefacility. FIG. 4C illustrates this process where, once the behavior ofthe air in the facility can be simulated, error minimization (module460) is applied over the trained model 445 (in step 412) to generateoptimal air-handling control instructions (465) that work within thetolerances of defined optimization targets 452, such as energy usage,particulate concentration, temperature, humidity, and pressure at everypoint in the facility, those targets being identified in step 408. Theoutput of the model 445 are air-handler control instructions 465, whichinstructions are then sent by the gateway 150 to the appropriateair-handling and distribution units via the mesh network 220. That is,the model 445 is able to set conditions (e.g., a schedule or activityinstructions) for all or a subset of the nodes 225 in a facility to actthe next X hours in order to reach an optimal result for the facility asa whole.

With reference to FIG. 5A, as just one example, the training data set ofa learning algorithm may include current (real-time) sensor datacomprising an HVAC state (from air-handler electrical current sensor₁data 125-A) 516, indoor temperature values (from air-quality sensor₁data 120-A) 512, and a predicted (from a third-party server) futureoutdoor temperature 514 (e.g., weather data) one hour in advance. Inaddition, historical data using a one-hour window sliding 15 minutes ata time is used to provide enough data to train the predictive model. Adesired outcome (supervisory variable) 532 of the generated model is thepredicted future indoor temperature one hour in the future. A supportvector regression learning algorithm with a polynomial kernel is appliedand produces, e.g., a variance score of 0.92. A trained air qualityprediction model 540 is generated, and error minimization is appliedover that trained model in FIG. 5B. FIG. 5B illustrates the generationof an indoor temperature by the trained model 540 over a range of HVACunits running and outdoor temperature. In general, the purpose of anerror minimization is to find the combination of inputs to a functionthat produces the least discrepancy from the desired optimizationtargets. With reference to FIG. 5A, an exemplary scenario may existwhere the set of variables the site administrator wants to optimize forare energy use and temperature difference from a setpoint. Theenvironment modeling logic 175 (or similar machine learning logic at thegateway 150, in other embodiments) obtains as inputs to the trained airquality prediction model 445 an HVAC state 516, an outdoor temperature514, and a current indoor temperature 512, and attempts to predict thefuture indoor temperature 532 one hour in advance. The optimizer 570 anderror function 560 apply an optimization routine that varies the HVACstate over a sliding window of 6 hours (that is, minimizing error over avarying number of hours into the future) to produce a set ofinstructions that, if applied, would result in the least HVAC use andleast error from the desired indoor temperature range. Of course, theforegoing is merely exemplary and other embodiments may use differentinput data sources and values, different optimization goals, and thelike.

In some embodiments, step 412 (FIG. 4A) may be performed at the server170 or another remote computing resource. In other embodiments, step 412may be performed at the gateway 150 itself, where server 170 transmitsthe trained model to the gateway 150, where it is stored in memory 340in model storage 368. In such an embodiment, performance at the gateway150 is not dependent on the availability of other WAN networks (such asthe Internet) so that even if an internet connection drops, thelast-trained model 445 can be applied to each set of generated sensorvalues to generate control decisions at the same relative frequency thatnew sensor data is collected, even where a new or updated model is notgenerated or received. That is, the processing of the error minimizationfunction 460 may in some embodiments be performed at the gatewayregardless of whether the gateway is “online” or “offline” (with respectto network connectivity) or a combination of the two. Such animplementation may be particularly well suited to facilities wherenetwork connection (outside of the local mesh network 220) may beunreliable or unavailable.

In the exemplary embodiment, the output of step 412 is a plurality ofinstructions to be respectively applied to one or more networked nodes225. In one embodiment, the instructions may include a set of tuples, orpairs, matching each node 225 with an instruction of varying complexity,and the gateway may transmit to each identified node its associatedinstruction. The transmissions to the relevant nodes 225 (that is, theair handling units that could function to change the air qualityconditions of the facility) are performed over the mesh network ofdevices 220. To this end and as shown in FIGS. 2B and 2C, pre-existingnetworked devices (such as lighting nodes) may be used to facilitatetransmission of air handling instructions over the mesh network,simplifying and increasing the reliability of the networkedcommunication.

The generated and transmitted instructions may be as simple as aninstruction to turn on/off the unit and/or an value to which the unitshould adjust its set operation (e.g., a temperature setpoint, speed ofrotation or activity, and so on). Additionally or alternately, theinstruction may be a schedule or timing for the node's activity (e.g.,the instructions may be a schedule for when an HVAC, MAU, baghouse,exhaust, etc. should come online/offline, whether based on an internalclock or a countdown/periodicity), or a rate or speed of itsfunctionality. In some embodiments, the instructions may apply to a node225 a broader, narrower, or otherwise different range of acceptablevalues in a range of tolerance (e.g., for a thermometer, a differentmin/max temperature range). In still other embodiments, the instructionsmay not set out specific values to be reached by a node 225 (e.g., a settemperature value) but may instead include information indicating thatthe node should apply a set of instructions stored at the node 225itself, for example to switch to and/or function in a certain presetmode or state.

The generated instructions apply, in the exemplary embodiment, to theentire set of networked devices 225 managed by the gateway 150 (oralternately, a subset of devices in a particular physical area,networked or organizational group, or the like). Where instructions aresent for each of the respective devices 225, the instructions mayindicate that a given node should continue to function in an unchangedor routine or normal manner. Upon each application of the trainedalgorithm, e.g., every 15 minutes or so or on a rolling or iterativebasis, a new set of instructions are generated and transmitted to thedevices within the facility. By these instructions, the single holisticmachine learning algorithm applied at the server 170 (or at the gateway150) functions to generate a plurality of optimized individual schedulesof each device to function in support of the air quality of the facilityas a whole.

In step 416, the gateway 150, and specifically, scheduling logic 352,uses the generated instructions to control the components of the airhandling system. In an exemplary embodiment, the gateway 150 obtainsdata from one or more electric current sensors 125 on a node 225 todetect when a node is on/off and when the power is up (and the degree towhich the node is operating). Based on this information, schedulinglogic 352 may function to determine whether to enable, disable, or alterthe operating status of a node to meet the conditions of the generatedinstructions. For instance, where a node 225 is a fan, scheduling logic352 may provide instructions to drive the fan with analog signals toinstruct a speed of action; that is, to enable or disable or rampup/down the speed of the fan where there is a variable power supply. Inthe case where node 225 is an HVAC unit, which units typically run basedon the measurement of a thermostat, the scheduling logic 352 may provideto the HVAC an electrical signal (e.g., a 24 volt AC signal) instructingthe unit whether to turn on/off, or when to turn components (e.g., stagecompressors) on/off. If no instructions are received (e.g., if thetransmission if lost or delayed), a node 225 may continue activity inits current manner (or according to its preset schedules) untilotherwise instructed.

The exemplary systems can be applied to existing hardware, and are notlimited to new installations of air handling devices or networks. One ormore existing air handling units 225 may be retrofit with electriccurrent sensors 125, such that no special compatibility or configurationof the nodes 225 is necessary. This may be the case even where an airhandling unit, lighting system, or other node 225 is not configured for,or is other otherwise unable to, communicate with gateway 150 on its ownto transmit sensor readings and receive instructions. For instance, thesystems described herein interface with an existing thermostat to readdata from the sensor (e.g., thermostat temperature data, humidity data,or the like) and transmit that data to the gateway 150 and ultimately tothe cloud (server 170). In an exemplary embodiment, the scheduling logic352 may interface the gateway with the node 225 via a relay to provideinstructions to the node. In another embodiment, the logic 352 mayinterface with a BACnet interface (e.g., of a thermostat) or anothertype of standard serial interface through which commands may beintroduced to the device. By these means, standard (predefined) commandscan be used that the device is already configured to process andintercept. In still another embodiment, a separate unit facilitatingwireless control of a node may be attached to the node. This may be, inone embodiment, a snap-on device with a dedicated set of circuitry tofacilitate the translation of control instructions from the gateway 150to the electrical components of the specific device at node 225 (e.g.,sensor, HVAC, baghouse, exhaust, MAU, lighting system, and so on).Alternately, the dedicated circuitry may be integral within the node 225(e.g., built into the thermostat, or other control circuitry).

In some embodiments, where instructions that meet air quality valueswithin the optimization targets cannot be generated (e.g., the targetsare too strict or the air quality is sufficiently far from those targetsdue to unusual circumstances), the module 460 may additionally oralternately output an error or notification to the facility manager,site administrator, or a designated third party, e.g., via text, SMS,email, light or sound alert on a device such as a cellular phone or PDA,voice or data message, light or sound notification within the facility(such as a warning light or siren), or the like. This notificationsystem may be, in some embodiments, integrated with an existingemergency system so as to meet industry-standard safety notification andalert requirements.

In addition to sensor data and aggregated sensor data, some embodimentsof the environment modeling logic 175 may calculate and take intoconsideration gradients of environmental factors through the facility110 or a portion of the facility 110, and the effect of those gradientson the modeled air. In a real-world implementation, a sensor reading ata particular sensor reflects an air quality condition that is notnecessarily localized to the area immediately around that sensor. Theflow of air through the facility may, for example, disperse and spreadcontaminants introduced into the air at a first location through afacility. Therefore, a single sensor reading at a first location mayimply a change in air quality at different locations in the facility.Tracking the flow of contaminants within a facility is a highly relevantfactor for consideration by an air quality predictive model. What ismore, a sensor reading at a particular sensor may reflect a conditionthat is ephemeral and likely to increase or decrease relatively quickly.Therefore, a rate of change in measured value may also be particularlyrelevant to a predicted condition of the air.

To facilitate this tracking, environment modeling logic 175 (or logic atthe gateway 150) may measure and store information indicative oftemperature gradients, humidity gradients, pressure gradients,contaminant concentration gradients, and/or other relevant quantitiesthat describe how air conditions change at a particular point and/orthrough different points in the facility. A temperature gradient valuefor example (measured in K/m, degrees/m, etc.) may reflect the directionand rate at which temperature may change the most rapidly at differentpoints in the facility, impacted for instance by relative location ofthe measurement in the three dimensional space (e.g., higher or lower),proximity to doors or windows, proximity to machinery, equipment, orother heat sources, and so on. In the exemplary embodiment, atemperature, or other, gradient from sensor to sensor may be determined.That is, a calculation of a change or delta that is informed based onthe measured readings at two or more sensors at known respectivelocations (such that a distance and direction between the sensors can beobtained) as compared to two discrete sensor readings at two discretelocations. Similarly, a rate and direction of change in air condition ata single location (of e.g., temperature, pressure, contaminants, etc.)may be considered a gradient to be determined from a sensor's readingsover time.

The measurement of different gradients can inform a stratification, orgrouping or assignment, of the facility into different behavioral sets.Stratification of the facility allows for a more comprehensive view ofbehavior of the facility, without assumptions of consistency orhomogeneity of air flow or environmental response. To illustrate,facility 110 (FIG. 1) is a 3-dimensional space. At different z-axispoints in the facility, the air pressure may vary. Similarly, variousareas of horizontal space, or angled or irregular space, may exist wherethe same type of measurement may vary in value at different locations.This variance is not in discrete, leveled layers of given heights, butrather, the air pressure may increase or decrease (whether regularly orirregularly) along a relative function of the z-axis value. A givengradient of an environmental conditions such as air pressure,temperature, humidity, contaminant level, and so on, may impact the flowof air, and therefore, an airborne contaminant, as will the type andintroduction point of the condition.

The calculation of the relevant gradient values may be performed by theenvironment modeling logic 175 or in some embodiments, by control logic350 (or exception handling logic 354) at the gateway 150, in a datagathering stage. In one embodiment, the gateway 150 may (in step 406 ofFIG. 4) calculate at least gradient such as a temperature gradient, acontaminant level gradient, and/or an air pressure gradient based onreceived sensor measurements. An unexpected gradient (e.g., a high rateof change) may trigger exception handling actions (step 420). In oneembodiment, the gateway 150 may send the calculated gradient(s) (or thedata from which calculation of gradient can be performed) to the server170, where logic 175 may use such data to model air flow. For instance,logic 175 may perform thermal stratification, air pressurestratification, and/or any other type of grouping or classification ofvarious physical locations or ranges within the facility 110. Thesecalculations, and/or the modeled flow of air, may be weighted andapplied in the execution of the generated air quality prediction modelin the manner described above with reference to FIGS. 3A-5B.

In an embodiment, the server 170 or the gateway 150 may contain hardwareand/or software programmed to calculate the rates of change intemperature, air pressure, humidity, and/or concentration ofcontaminants across a physical space, such as a room, a building withmultiple rooms, a horizontal or vertical area, or the like. These ratesof change may occur at a single point (multiple measurements at onesensor at different times) or between multiple sensors at differentlocations in the space (whether at a single timestamp or over multiplepoints in time). These rates of changes are gradients used to predicthow the air in a facility will behave in the near future. By thesemeans, calculated rates of change, directions or flows of change, andother computed gradient values can be derived from sensed values oftemperature, pressure, humidity, and contaminant concentration, and suchcalculations may be used in a predictive analysis to model the flow ofair and/or particulates within the facility.

As an example, the pressure measured by sensors throughout a facilitymay be used to determine pressure gradients, and this information can beused to predict how air and, thus, contaminants will flow through thefacility. In this regard, after the containment levels throughout thefacility have been determined based on measurements from the sensors,the server 170 or the gateway 150 may use pressure gradient informationto predict how these contaminant levels will change (e.g., how thecontaminants will flow through the facility) in the future. For example,using the current sensor readings and the pressure gradientcalculations, the server 170 or the gateway 150 may model the flow ofcontaminants and then evaluate such model to predict when thecontaminant levels in a certain area will exceed a desired concentrationif corrective action is not taken. In response to a prediction that adesired concentration level will be exceeded or other undesiredcondition will occur, the server 170 or gateway may take correctiveaction, such as activation of one or more fans or other types of airhandling equipment or a change to one or more setpoints, in an attemptto avoid or otherwise mitigate the predicted condition. Similartechniques may be used to control other types of conditions orparameters in a proactive manner. As an example, temperature gradientsmay be used to predict how temperatures within the facility will changein the future, and an action may be taken to proactively controltemperature, such as activation of one or more fans or other types ofair handling equipment or a change to one or more setpoints, to avoid ormitigate an undesired temperature condition.

In some cases, calculated gradients may be used to sense certainconditions for which a corrective action is desired. As an example, ifthe gradient between two temperatures, such as a temperature measured bya sensor at a lower altitude and temperature measured by a sensor at ahigh altitude in the same room or area of the facility exceeds athreshold, a particular action may be taken, such as activation of a fanor other air handling equipment or a change to one or more setpoints.The action taken may be selected to reduce or other change the measuredgradient. Similarly, an action may be taken to reduce or otherwisechange a measured pressure gradient between the pressures measured bydifferent sensors in different areas of the facility or between apressure measured inside of the facility and atmospheric pressuremeasured outside of the facility.

The calculations of gradients (e.g. a rate of change) may be performedat the server 170 or the gateway 150. In a case where the calculationsare performed at the gateway 150, the gateway 150 may comprise, in thememory 340, a cache storing sensor readings over a relatively smallperiod of time (e.g., the most recent minutes, hours, or days), to beused in rate of change calculations. In some embodiments, sensor data inthis cache beyond a certain “age” may be overwritten with newer sensordata so as to avoid unneeded storage of older data not relevant to thegradient analysis (such older data being stored in the time-seriesdatabase 160). Where the calculations are performed at the server 170,recent sensor data may be obtained from the time-series database 160and/or the gateway 150.

The calculated gradient information, including changes in any oftemperature, pressure, and so on, is transmitted to the logic 175 to beused as an input to the predictive model both when the model is beingtrained and when the trained model is applied to real-time sensor data.In some embodiments, where the execution of the machine learninganalysis with the trained model is performed at the gateway 150, nonetworked transmission of the gradient data is needed.

Conventional systems focus control of an air handling system on thecomfort of the human occupants of the physical space. Further,conventional industrial applications environmental systems fail toconsider sensed environmental data both individually and holistically.That is, sensors (e.g., thermometers) controlling an HVAC unit arelimited to the universe of data collected by the individual sensor andthe application of, e.g., empirical formulas thereto. Even if theactions of another HVAC unit would influence the environmental healthand safety of the area around neighboring sensors, the air handlingsystems in conventional applications are incapable of coordinatingknowledge and action between the sensors and attached air handling ordistribution units.

The systems and methods described above allow for automated intelligentcontrol of multiple individually-controlled air handling units insupport of overall environmental health and safety while optimizing forthe goals of the site administrator. More specifically, the systems andmethods described herein apply machine learning to learn how thefacility as a whole behaves as a cohesive unit. Further, the describedmachine learning algorithms that optimize for any of several differentgoals, a facility-specific and industry-specific set of rules can besustained. Because of this, air handling systems that use a computersystem employing the solutions described herein perform moreefficiently, faster (with reduced execution time) and with less wastedcomputing resources, and in safer and more sustainable, energy-efficientmanners. Accordingly, the uptime of the both the computing systems andthe overall machine systems within the facility is improved.

The foregoing is merely illustrative of the principles of thisdisclosure and various modifications may be made by those skilled in theart without departing from the scope of this disclosure. Theabove-described embodiments are presented for purposes of illustrationand not of limitation. The present disclosure also can take many formsother than those explicitly described herein. Accordingly, it isemphasized that this disclosure is not limited to the explicitlydisclosed methods, systems, and apparatuses, but is intended to includevariations to and modifications thereof, which are within the spirit ofthe following claims.

As a further example, variations of apparatus or process parameters(e.g., dimensions, configurations, components, process step order, etc.)may be made to further optimize the provided structures, devices andmethods, as shown and described herein. In any event, the structures anddevices, as well as the associated methods, described herein have manyapplications. Therefore, the disclosed subject matter should not belimited to any single embodiment described herein, but rather should beconstrued in breadth and scope in accordance with the appended claims.

Now, therefore, the following is claimed:
 1. A method for modeling thebehavior and condition of air in a structure, the method comprising:receiving a first environmental measurement from a first sensor and asecond environmental measurement from a second sensor, wherein the firstsensor and the second sensor are fixedly positioned within thestructure; generating a predictive model for air quality within thestructure based on (i) a dataset comprising the first environmentalmeasurement, the second environmental measurement, and a plurality ofstates for each of a plurality of air handling units for the structureand (ii) one or more environmental health and safety standards, whereinthe generating includes: (a) determining, based on the dataset, aplurality of parameters that reflect a condition of air in thestructure; and (b) applying a plurality of weighted values to theplurality of parameters, respectively, the weighted values beingobtained through the application of one or more supervised learningalgorithms; applying error minimization over the predictive model toadhere to a desired air quality optimization target within a certaintime period; generating, through the predictive model, a plurality ofair-handling control instructions for respectively controlling theoperation of each of the plurality of air handling units; andtransmitting, to each of the plurality of air handling units, at least arespective one of the generated air-handling control instructions. 2.The method of claim 1, wherein the one or more supervised learningalgorithms comprise a support vector regression model.
 3. The method ofclaim 1, wherein the first environmental measurement relates to one ormore of: temperature, air pressure, humidity, contaminant level,volatile organic compounds, carbon monoxide, particulates, and energyusage.
 4. The method of claim 1, wherein an electric current sensor iscoupled to one of the plurality of air handling units, and wherein ameasurement by the electric current sensor reflects a state of the oneof the plurality of air handling units.
 5. The method of claim 1,further comprising: receiving a third environmental measurement from athird sensor positioned on the exterior of the structure.
 6. The methodof claim 1, wherein the dataset further comprises weather forecast dataregarding a geographic location at which the structure is located. 7.The method of claim 1, wherein the dataset further comprises at leastone measurement of air contaminant flow within the structure; andwherein the measurement of air contaminant flow is calculated based on adelta change in an air quality measurement between the firstenvironmental measurement and the second environmental measurement, adistance between the first sensor and the second sensor, and a relativeposition between the first sensor and the second sensor.
 8. The methodof claim 1, wherein the dataset further comprises at least onemeasurement of air contaminant flow within the structure; and whereinthe measurement of air contaminant flow is calculated based on an airquality gradient.
 9. The method of claim 1, wherein the firstenvironmental measurement is at least one of a temperature gradient, anair pressure gradient, or a contaminant concentration gradient, andwherein the first environmental measurement is determined by calculatinga rate of change between (a) measured readings at two or more sensors atknown respective locations or (b) measured readings at a single sensorat two or more points in time.
 10. The method of claim 1, wherein thefirst environmental measurement is a contaminant concentration, whereinthe generating, through the predictive model, of the plurality ofair-handling control instructions comprises modeling an air contaminantflow within the structure, and wherein the first environmentalmeasurement is used as input to the predictive model in modeling the aircontaminant flow.
 11. The method of claim 1, further comprisingcalculating a gradient between the first environmental measurement andthe second environmental measurement, wherein the predictive model isbased on the calculated gradient.
 12. The method of claim 1, wherein theplurality of air handling units includes at least one heating,ventilation and air conditioning (HVAC) unit, at least one make-up airunit (MAU), at least one baghouse, and at least one fan.
 13. The methodof claim 12, wherein the at least one fan includes an exhaust fan and ade-stratification fan.
 14. The method of claim 1, wherein the firstenvironmental measurement is at least one of a temperature gradient, anair pressure gradient, or a contaminant concentration gradient, andwherein the first environmental measurement is determined by calculatinga rate of change between measured readings at two or more sensors atknown respective locations.
 15. An air handling system comprising: anair handling control unit having at least one processor; one or moresensors positioned within an indoor facility and communicatively coupledto the air handling control unit via a network, each of the one or moresensors being configured to sense an environmental measurement directedto air quality or a state of an air handling unit of a plurality of airhandling units, and to transmit the sensed environmental measurement orthe state to the air handling control unit, the frequency of periodictransmission being based on a predetermined interval of time; theplurality of air handling units communicatively coupled to the airhandling control unit via the network, each of the plurality of airhandling units comprising a communication interface permittingcommunication on the network and at least one control interface forcontrolling the air handling unit; a server communicatively coupled tothe air handling control unit; wherein the air handling control unit isprogrammed with instructions that, when executed by the at least oneprocessor, cause the at least one processor to communicate over thenetwork and store, in a database, in association with a time of sensing,sensed environmental measurements received from the one or more sensors;wherein the server is configured to: (1) generate a training dataset inaccordance with environmental measurements and states of the pluralityof air handling units obtained from a database and one or moreenvironmental health and safety standards; (2) train an air qualityprediction model based on the training dataset through the applicationof one or more supervised learning algorithms; (3) transmit, to the airhandling control unit, the trained air quality prediction model; and (4)repeat steps (1)-(3) in an iterative manner in accordance with thepredetermined interval of time; and wherein the instructions, whenexecuted by the at least one processor, cause the at least one processorto: (a) receive one or more subsequent sensed environmental measurementsfrom the one or more sensors; (b) apply the trained air qualityprediction model to the one or more subsequent sensed environmentalmeasurements to obtain a predicted air quality condition of the indoorfacility; (c) generate air handling control instructions for arespective each of the control interfaces of the plurality of airhandling units in accordance with the predicted air quality condition ofthe indoor facility; and (d) transmit, to the communication interface ofeach of the plurality of air handling units, respective air handlingcontrol instructions.
 16. The air handling system of claim 15, whereinan environmental measurement relates to one or more of: temperature, airpressure, humidity, contaminant level, volatile organic compounds,carbon monoxide, particulates, and energy usage.
 17. The air handlingsystem of claim 15, wherein the one or more sensors comprise an electriccurrent sensor coupled to one of the plurality of air handling units,and wherein a measurement by the electric current sensor reflects astate of the one of the plurality of air handling units.
 18. The airhandling system of claim 15, wherein the dataset is further generated inaccordance with weather forecast data regarding a geographic location atwhich the facility is located.
 19. The air handling system of claim 15,wherein the dataset is further generated in accordance with at least onemeasurement of air contaminant flow within the structure; and whereinthe measurement of air contaminant flow is calculated based on a deltachange in an air quality measurement between environmental measurementsof two of the one or more sensors, a distance between the two of the oneor more sensors, and a relative position between the two of the one ormore sensors.
 20. The air handling system of claim 15, wherein thedataset is further generated in accordance with at least one measurementof air contaminant flow within the structure; and wherein themeasurement of air contaminant flow is calculated based on an airquality gradient.
 21. The air handling system of claim 15, wherein theinstructions, when executed by the at least one processor, cause the atleast one processor to determine, based on the one or more subsequentsensed environmental measurements, an air quality gradient, the airquality gradient being at least one of a temperature gradient, an airpressure gradient, or a contaminant concentration, and wherein the airquality gradient is determined by calculating a rate of change between(a) measured readings at two or more sensors at known respectivelocations or (b) measured readings at a single sensor at two or morepoints in time.
 22. The method of claim 21, wherein the trained airquality prediction model is applied to both (a) the one or moresubsequent sensed environmental measurements and (b) the calculated airquality gradient to model an air contaminant flow within the indoorfacility.
 23. The system of claim 15, wherein the server is configuredto detect an exception based on the one or more environmentalmeasurements and transmit, to the communication interface of at leastone of the plurality of air handling units, one or more instructions forinstructing the at least one of the plurality of air handling units toperform a predefined action.
 24. The system of claim 15, wherein theplurality of air handling units includes at least one heating,ventilation and air conditioning (HVAC) unit, at least one make-up airunit (MAU), at least one baghouse, and at least one fan.
 25. A systemfor modeling the behavior and condition of air in a structure, thesystem comprising: a plurality of nodes, each node comprising (a) acommunication interface permitting communication on a first network and(b) one or more sensors, each of the one or more sensors beingrespectively configured to take an environmental measurement or a statemeasurement of an industrial air handling device of a plurality ofindustrial air handling devices, wherein the environmental measurementis directed to one or more of: temperature, air pressure, humidity,contaminant level, volatile organic compounds, carbon monoxide,particulates, and energy usage; a management device communicativelycoupled to the first network, the management device having at least oneprocessor programmed with instructions that, when executed by the atleast one processor, cause the at least one processor to: receive afirst environmental measurement from a first node of the plurality ofnodes and a second environmental measurement of a second node of theplurality of nodes; obtain a predictive model for air quality within thestructure, the predictive model having been trained in accordance with adataset comprising historical environmental measurements from both ofthe first node and the second node and state measurements for theplurality of industrial handling devices; apply error minimization overthe predictive model to adhere to a desired air quality optimizationtarget within a certain time period; generate a plurality ofair-handling control instructions for respectively controlling theplurality of industrial air handling devices; and transmit, to each ofthe plurality of industrial air handling devices, a respective set ofthe generated air-handling control instructions.
 26. The system of claim25, wherein the plurality of air handling units includes at least oneheating, ventilation and air conditioning (HVAC) unit, at least onemake-up air unit (MAU), at least one baghouse, and at least one fan.